Retrieve Taxonomic Data From WoRMS

WoRMS

The World Register of Marine Species (WoRMS) is a comprehensive database providing authoritative lists of marine organism names, managed by taxonomic experts. It combines data from the Aphia database and other sources like AlgaeBase and FishBase, offering species names, higher classifications, and additional data. WoRMS is continuously updated and maintained by taxonomists. In this tutorial, we source the R package worrms to access WoRMS data for our function. Please note that the authors of SHARK4R are not affiliated with WoRMS.

Getting Started

Installation

You can install the latest version of SHARK4R from CRAN using:

install.packages("SHARK4R")

Load the SHARK4R, dplyr and ggplot2 libraries:

library(SHARK4R)
library(dplyr)
library(ggplot2)

Retrieve Data Using SHARK4R

Retrieve Phytoplankton Data From SHARK

Phytoplankton data, including scientific names and AphiaIDs, are downloaded from SHARK. To see more download options, please visit the Retrieve Data From SHARK tutorial.

# Retrieve all phytoplankton data from April 2015
shark_data <- get_shark_data(fromYear = 2015, 
                             toYear = 2015,
                             months = 4, 
                             dataTypes = "Phytoplankton",
                             verbose = FALSE)

Match Taxa Names

Taxon names can be matched with the WoRMS API to retrieve Aphia IDs and corresponding taxonomic information. The match_worms_taxa() function incorporates retry logic to handle temporary failures, ensuring that all names are processed successfully.

# Find taxa without Aphia ID
no_aphia_id <- shark_data %>%
  filter(is.na(aphia_id))

# Randomly select taxa with missing aphia_id
taxa_names <- sample(unique(no_aphia_id$scientific_name), 
                     size = 10,
                     replace = TRUE)

# Match taxa names with WoRMS
worms_records <- match_worms_taxa(unique(taxa_names),
                                  fuzzy = TRUE,
                                  best_match_only = TRUE,
                                  marine_only = TRUE,
                                  verbose = FALSE)

# Print result as tibble
tibble(worms_records)
## # A tibble: 3 × 29
##   name  AphiaID url   scientificname authority status unacceptreason taxonRankID
##   <chr>   <int> <chr> <chr>          <chr>     <chr>  <chr>                <int>
## 1 Dipl…  109515 http… Diplopsalis    R.S.Berg… accep… <NA>                   180
## 2 Cyli…  149004 http… Cylindrotheca… (Ehrenbe… accep… <NA>                   220
## 3 Unic…      NA <NA>  <NA>           <NA>      no co… <NA>                    NA
## # ℹ 21 more variables: rank <chr>, valid_AphiaID <int>, valid_name <chr>,
## #   valid_authority <chr>, parentNameUsageID <int>, originalNameUsageID <int>,
## #   kingdom <chr>, phylum <chr>, class <chr>, order <chr>, family <chr>,
## #   genus <chr>, citation <chr>, lsid <chr>, isMarine <int>, isBrackish <int>,
## #   isFreshwater <int>, isTerrestrial <int>, isExtinct <lgl>, match_type <chr>,
## #   modified <chr>

Get WoRMS records from AphiaID

Taxonomic records can also be retrieved using Aphia IDs, employing the same retry and error-handling logic as the match_worms_taxa() function.

# Randomly select ten Aphia IDs
aphia_ids <- sample(unique(shark_data$aphia_id), 
                    size = 10)

# Remove NAs
aphia_ids <- aphia_ids[!is.na(aphia_ids)]

# Retrieve records
worms_records <- get_worms_records(aphia_ids,
                                   verbose = FALSE)

# Print result as tibble
tibble(worms_records)
## # A tibble: 9 × 28
##   AphiaID url   scientificname authority status unacceptreason taxonRankID rank 
##     <int> <chr> <chr>          <chr>     <chr>  <lgl>                <int> <chr>
## 1  106283 http… Plagioselmis   Butcher … accep… NA                     180 Genus
## 2  149044 http… Melosira numm… C.Agardh… unass… NA                     220 Spec…
## 3  233369 http… Peridiniella … (Paulsen… accep… NA                     220 Spec…
## 4  248149 http… Pseudopedinel… Skuja, 1… accep… NA                     220 Spec…
## 5  177595 http… Romeria        M.Koczwa… accep… NA                     180 Genus
## 6  178611 http… Oocystis       Nägeli e… accep… NA                     180 Genus
## 7  110223 http… Protoperidini… (Ostenfe… accep… NA                     220 Spec…
## 8  149297 http… Chaetoceros c… Ostenfel… unass… NA                     220 Spec…
## 9  109553 http… Protoperidini… Bergh, 1… accep… NA                     180 Genus
## # ℹ 20 more variables: valid_AphiaID <int>, valid_name <chr>,
## #   valid_authority <chr>, parentNameUsageID <int>, originalNameUsageID <int>,
## #   kingdom <chr>, phylum <chr>, class <chr>, order <chr>, family <chr>,
## #   genus <chr>, citation <chr>, lsid <chr>, isMarine <int>, isBrackish <int>,
## #   isFreshwater <int>, isTerrestrial <int>, isExtinct <int>, match_type <chr>,
## #   modified <chr>

Get WoRMS Taxonomy

SHARK sources taxonomic information from Dyntaxa, which is reflected in columns starting with taxon_xxxxx. Equivalent columns based on WoRMS can be retrieved using the add_worms_taxonomy() function.

# Retrieve taxonomic table
worms_taxonomy <- add_worms_taxonomy(aphia_ids,
                                     verbose = FALSE)

# Print result as tibble
tibble(worms_taxonomy)
## # A tibble: 9 × 10
##   aphia_id worms_scientific_name    worms_kingdom worms_phylum     worms_class  
##      <dbl> <chr>                    <chr>         <chr>            <chr>        
## 1   106283 Plagioselmis             Chromista     Cryptophyta      Cryptophyceae
## 2   149044 Melosira nummuloides     Chromista     Heterokontophyta Bacillarioph…
## 3   233369 Peridiniella danica      Chromista     Myzozoa          Dinophyceae  
## 4   248149 Pseudopedinella elastica Chromista     Ochrophyta       Dictyochophy…
## 5   177595 Romeria                  Bacteria      Cyanobacteria    Cyanophyceae 
## 6   178611 Oocystis                 Plantae       <NA>             Trebouxiophy…
## 7   110223 Protoperidinium granii   Chromista     Myzozoa          Dinophyceae  
## 8   149297 Chaetoceros ceratosporus Chromista     Heterokontophyta Bacillarioph…
## 9   109553 Protoperidinium          Chromista     Myzozoa          Dinophyceae  
## # ℹ 5 more variables: worms_order <chr>, worms_family <chr>, worms_genus <chr>,
## #   worms_species <chr>, worms_hierarchy <chr>
# Enrich data with data from WoRMS
shark_data_with_worms <- shark_data %>%
  left_join(worms_taxonomy, by = "aphia_id")

Retrieve WoRMS Taxonomic Hierarchies

To explore the full hierarchical taxonomy records of your Aphia IDs, you can use the get_worms_taxonomy_tree() function. This function retrieves records for the entire taxonomic tree from WoRMS, including parent-child relationships, and can optionally fetch all descendants (e.g. species) under a genus or known synonyms.

# Retrieve taxonomic tree
worms_tree <- get_worms_taxonomy_tree(
  aphia_ids[1],                # use first id only in this example
  add_descendants = FALSE,     # only retrieve hierarchy for given AphiaIDs
  add_synonyms = FALSE,        # do not retrieve synonyms
  verbose = FALSE              # suppress progress messages
)

# Print as tibble for easier viewing
tibble(worms_tree)
## # A tibble: 7 × 28
##   AphiaID url   scientificname authority status unacceptreason taxonRankID rank 
##     <int> <chr> <chr>          <chr>     <chr>  <lgl>                <int> <chr>
## 1       7 http… Chromista      <NA>      accep… NA                      10 King…
## 2  582418 http… Hacrobia       <NA>      accep… NA                      20 Subk…
## 3   17638 http… Cryptophyta    Cavalier… accep… NA                      30 Phyl…
## 4   17639 http… Cryptophyceae  Fritsch,… accep… NA                      60 Class
## 5   20997 http… Pyrenomonadal… G. Novar… accep… NA                     100 Order
## 6   22582 http… Geminigeraceae B.L. Cla… accep… NA                     140 Fami…
## 7  106283 http… Plagioselmis   Butcher … accep… NA                     180 Genus
## # ℹ 20 more variables: valid_AphiaID <int>, valid_name <chr>,
## #   valid_authority <chr>, parentNameUsageID <int>, originalNameUsageID <lgl>,
## #   kingdom <chr>, phylum <chr>, class <chr>, order <chr>, family <chr>,
## #   genus <chr>, citation <chr>, lsid <chr>, isMarine <int>, isBrackish <int>,
## #   isFreshwater <int>, isTerrestrial <int>, isExtinct <lgl>, match_type <chr>,
## #   modified <chr>

Assign Phytoplankton Groups

Phytoplankton data are often categorized into major groups such as Dinoflagellates, Diatoms, Cyanobacteria, and Others. This grouping can be achieved by referencing information from WoRMS and assigning taxa to these groups based on their taxonomic classification, as demonstrated in the example below.

# Subset data from one national monitoring station
nat_stations <- shark_data %>%
  filter(station_name %in% c("BY5 BORNHOLMSDJ"))

# Randomly select one sample from the nat_stations
sample <- sample(unique(nat_stations$shark_sample_id_md5), 1)

# Subset the random sample
shark_data_subset <- shark_data %>%
  filter(shark_sample_id_md5 == sample)

# Assign groups by providing both scientific name and Aphia ID
plankton_groups <- assign_phytoplankton_group(
  scientific_names = shark_data_subset$scientific_name,
  aphia_ids = shark_data_subset$aphia_id,
  verbose = FALSE)

# Print result
tibble(distinct(plankton_groups))
## # A tibble: 23 × 2
##    scientific_name      plankton_group 
##    <chr>                <chr>          
##  1 Pauliella taeniata   Diatoms        
##  2 Amylax triacantha    Dinoflagellates
##  3 Aphanocapsa          Cyanobacteria  
##  4 Aphanothece          Cyanobacteria  
##  5 Chaetoceros similis  Diatoms        
##  6 Dinobryon balticum   Other          
##  7 Dinophysis acuminata Dinoflagellates
##  8 Dinophysis norvegica Dinoflagellates
##  9 Gymnodinium          Dinoflagellates
## 10 Protodinium simplex  Other          
## # ℹ 13 more rows
# Add plankton groups to data and summarize abundance results
plankton_group_sum <- shark_data_subset %>%
  mutate(plankton_group = plankton_groups$plankton_group) %>%
  filter(parameter == "Abundance") %>%
  group_by(plankton_group) %>%
  summarise(sum_plankton_groups = sum(value, na.rm = TRUE))

# Plot a pie chart
ggplot(plankton_group_sum, 
       aes(x = "", y = sum_plankton_groups, fill = plankton_group)) +
  geom_col(width = 1) +
  coord_polar(theta = "y") +
  labs(
    title = "Phytoplankton Groups",
    subtitle = paste(unique(shark_data_subset$station_name),
                     unique(shark_data_subset$sample_date)),
    fill = "Plankton Group"
  ) +
  theme_void() +
  theme(plot.background = element_rect(fill = "white", color = NA))

Assign Custom Phytoplankton Groups

You can add custom plankton groups by using the custom_groups parameter, allowing flexibility to categorize plankton based on specific taxonomic criteria. Please note that the order of the list matters: taxa are assigned to the last matching group. For example: Mesodinium rubrum will be excluded from the Ciliates group because it appears after Ciliates in the list in the example below.

# Define custom plankton groups using a named list
custom_groups <- list(
  "Cryptophytes" = list(class = "Cryptophyceae"),
  "Green Algae" = list(class = c("Trebouxiophyceae", 
                                 "Chlorophyceae", 
                                 "Pyramimonadophyceae"),
                       phylum = "Chlorophyta"),
  "Ciliates" = list(phylum = "Ciliophora"),
  "Mesodinium rubrum" = list(scientific_name = "Mesodinium rubrum"),
  "Dinophysis" = list(genus = "Dinophysis")
)

# Assign groups by providing scientific name only, and adding custom groups
plankton_groups <- assign_phytoplankton_group(
  scientific_names = shark_data_subset$scientific_name,
  custom_groups = custom_groups,
  verbose = FALSE)

# Add new plankton groups to data and summarize abundance results
plankton_custom_group_sum <- shark_data_subset %>%
  mutate(plankton_group = plankton_groups$plankton_group) %>%
  filter(parameter == "Abundance") %>%
  group_by(plankton_group) %>%
  summarise(sum_plankton_groups = sum(value, na.rm = TRUE))

# Plot a new pie chart, including the custom groups
ggplot(plankton_custom_group_sum, 
       aes(x = "", y = sum_plankton_groups, fill = plankton_group)) +
  geom_col(width = 1) +
  coord_polar(theta = "y") +
  labs(
    title = "Phytoplankton Custom Groups",
    subtitle = paste(unique(shark_data_subset$station_name),
                     unique(shark_data_subset$sample_date)),
    fill = "Plankton Group"
  ) +
  theme_void() +
  theme(plot.background = element_rect(fill = "white", color = NA))

Citation

## To cite package 'SHARK4R' in publications use:
## 
##   Lindh, M. and Torstensson, A. (2025). SHARK4R: Accessing and
##   Validating Marine Environmental Data from 'SHARK' and Related
##   Databases. R package version 1.0.1.
##   https://CRAN.R-project.org/package=SHARK4R
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {SHARK4R: Accessing and Validating Marine Environmental Data from 'SHARK' and Related Databases},
##     author = {Markus Lindh and Anders Torstensson},
##     year = {2025},
##     note = {R package version 1.0.1},
##     url = {https://CRAN.R-project.org/package=SHARK4R},
##   }